An integrated approach of remote sensing and geospatial analysis for modeling and predicting the impacts of climate change on food security

Abstract

The agriculture sector provides the majority of food supplies, ensures food security, and promotes sustainable development. Due to recent climate changes as well as trends in human population growth and environmental degradation, the need for timely agricultural information continues to rise. This study analyzes and predicts the impacts of climate change on food security (FS). For 2002–2021, Landsat, MODIS satellite images and predisposing variables (land surface temperature (LST), evapotranspiration, precipitation, sunny days, cloud ratio, soil salinity, soil moisture, groundwater quality, soil types, digital elevation model, slope, and aspect) were used. First, we used a deep learning convolutional neural network (DL-CNN) based on the Google Earth Engine (GEE) to detect agricultural land (AL). A remote sensing-based approach combined with the analytical network process (ANP) model was used to identify frost-affected areas. We then analyzed the relationship between climatic, geospatial, and topographical variables and AL and frost-affected areas. We found negative correlations of − 0.80, − 0.58, − 0.43, and − 0.45 between AL and LST, evapotranspiration, cloud ratio, and soil salinity, respectively. There is a positive correlation between AL and precipitation, sunny days, soil moisture, and groundwater quality of 0.39, 0.25, 0.21, and 0.77, respectively. The correlation between frost-affected areas and LST, evapotranspiration, cloud ratio, elevation, slope, and aspect are 0.55, 0.40, 0.52, 0.35, 0.45, and 0.39. Frost-affected areas have negative correlations with precipitation, sunny day, and soil moisture of − 0.68, − 0.23, and − 0.38, respectively. Our findings show that the increase in LST, evapotranspiration, cloud ratio, and soil salinity is associated with the decrease in AL. Additionally, AL decreases with a decreasing in precipitation, sunny days, soil moisture, and groundwater quality. It was also found that as LST, evapotranspiration, cloud ratio, elevation, slope, and aspect increase, frost-affected areas increase as well. Furthermore, frost-affected areas increase when precipitation, sunny days, and soil moisture decrease. Finally, we predicted the FS threat for 2030, 2040, 2050, and 2060 using the CA–Markov method. According to the results, the AL will decrease by 0.36% from 2030 to 2060. Between 2030 and 2060, however, the area with very high frost-affected will increase by about 10.64%. In sum, this study accentuates the critical impacts of climate change on the FS in the region. Our findings and proposed methods could be helpful for researchers to model and quantify the climate change impacts on the FS in different regions and periods.

Climate Crisis and Agricultural Response: Climate Resilient Crops for Sustainability in Food Production Systems

“…Therefore, climate change added to population explosion will have detrimental effect on food security and sustainability of the agricultural production systems [8]. Higher temperatures will result in proliferation of weed and insect populations and encourage emergence of new races of pathogens and biotypes of insects [9]. Absence of rains lead to crop failures and will increase cases of farmers suicides.…”

Modeling and Estimating the Land Surface Temperature (LST) Using Remote Sensing and Machine Learning (Case Study: Yazd, Iran)

“…Land surface temperature (LST) is a key determinant of the energy balance in physical processes occurring on the earth's surface [14,15]. The study of LST, along with other factors like evapotranspiration and soil salinity, proves valuable in examining the effects of global warming on food security, energy consumption, carbon sinks [16,17], and finally, human well-being [18,19].…”

Using Time Series Sentinel Images for Object-Oriented Crop Extraction of Planting Structure in the Google Earth Engine

“…Agriculture is the foundation of social and economic development. Crop planting structure can reflect the current situation of agricultural development in a region [1,2]. Extracting crop planting structure information timely and accurately is key to monitoring field crops, forecasting crops yield, water-saving irrigation, evaluation of agricultural non-point-source pollution, etc.…”